Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese. To address this gap, we construct **Chumor**, the first and the largest Chinese humor explanation dataset. **Chumor** is sourced from Ruo Zhi Ba (RZB, 弱智吧), a Chinese Reddit-like platform known for sharing intellectually challenging and culturally specific jokes. We test ten LLMs through direct and chain-of-thought prompting, revealing that **Chumor** poses significant challenges to existing LLMs, with their accuracy slightly above random and far below human. In addition, our analysis highlights that human-annotated humor explanations are significantly better than those generated by GPT-4o and ERNIE4-turbo. We release **Chumor** at https://huggingface.co/datasets/MichiganNLP/Chumor , our project page is at https://github.com/MichiganNLP/Chumor-2.0 , our leaderboard is at https://huggingface.co/spaces/MichiganNLP/Chumor-leaderboard , and our codebase is at https://github.com/MichiganNLP/Chumor-2.0 .

Large Language Models (LLMs) have demon- strated exceptional performance across diverse tasks. To harness their capabilities for Text- to-SQL, we introduce R3 (Review-Rebuttal- Revision), a consensus-based multi-agent sys- tem for Text-to-SQL tasks. R3 achieves the new state-of-the-art performance of 89.9 on the Spider test set. In the meantime, R3 achieves 61.80 on the Bird development set. R3 out- performs existing single-LLM and multi-agent Text-to-SQL systems by 1.3% to 8.1% on Spi- der and Bird, respectively. Surprisingly, we find that for Llama-3-8B, R3 outperforms chain-of- thought prompting by over 20%, even outper- forming GPT-3.5 on the Spider development set. We open-source our codebase at https: //github.com/1ring2rta/R3.